When customers place an order, they expect perfection. Having grown accustomed to ordering online and waking up the next morning to the box on their porch, customers have high expectations that every order will be perfect. But manufacturers have long felt that perfect order performance is an admirable yet unattainable goal.
Perfect order performance is a key performance indicator that strongly predicts customer satisfaction. It doesn’t matter if you get it there on time if all parts of the order aren’t there, if something is broken, or if your customer was expecting a discount but didn’t get it. In today’s instant-gratification world, consumers consider “almost right” completely wrong.
Four separate factors contribute to achieving a “perfect order”: the delivery must arrive complete, on-time, undamaged, and with proper documentation. Organizations that achieve a 99% score in each of these areas still only score a 96% in perfect order performance overall. This is what process experts call the “multiplier effect,” and it’s a result of how all the contributing factors are calculated to create a final score:
Perfect order performance = (percentage of orders delivered complete X percentage of orders delivered on time X percentage of orders received damage-free X percentage of orders with correct documentation) X 100
According to APQC Open Standards Benchmarking data, organizations that are median, or midpoint, performers on this measure have a perfect order index of 90. In other words, 10% of orders being shipped by companies who are median performers have some form of failure or defect, so 10% of order recipients are disappointed. Performers in the top quartile achieve a 95 or better perfect order index.
The customer’s view of what constitutes a “perfect” order matters… a lot. It’s why many organizations have a love/hate relationship with perfect order performance as a measure of process efficiency. They recognize that it doesn’t take much to get it wrong, but feel that it may be impossible to get it exactly right. However, organizations that excel on this measure have an advantage that others don’t: They are more likely to incorporate more automation into their supply chain processes, reducing human error and improving order cycle times.
Digital Transformation
Order fulfillment typically involves cooperation and coordination of logistics activities across multiple organizational silos. Moving the needle closer to perfect order performance requires getting a firm grasp on every subprocess within the end-to-end fulfillment process, ensuring that orders are taken correctly, inventory is there when needed, products are packaged well and delivered on time, and accurate documentation is provided. Adding in automation takes much of the human error out of these processes, and can also make logistics faster, more accurate and more effective.
In 2018, almost every manufacturing organization we encounter is in the middle of some kind of digital transformation. Everyone is buzzing about Big Data, where it’s coming from, and how best to capture and analyze it. One of the biggest benefits of the digitization of the supply chain is that, when combined with process improvement, perfect order performance is no longer a CEO’s pipe dream.
According to APQC benchmarking data, the organizations that achieve high levels of perfect order performance have several best practices in common, most of which depend on technology and data:
Plan for demand. Top performers in perfect order performance use technology to get closer to their customers. By analyzing purchasing patterns and sharing data with customers, they understand what customers need and when they need it, so they can accurately forecast demand and schedule production to meet it.
Ensure reliable data. Accurate customer data and customer specifications help ensure that the right order is delivered to the right place at the right time. Accurate product master data helps you recommend the best product for the customer’s needs, and ensures the end user receives proper documentation and an undamaged product that was appropriately packaged.
Share data. Sharing data with your suppliers helps you work together to get products to customers. Tracking asset management data helps manufacturers keep up with preventative maintenance, preventing downtime and production interruption. Data sharing also enables real-time information about production status for a more agile production schedule.
Automate orders. Sales order automation eliminates data entry errors that can lead to inaccurate orders and shipping delays. The top quartile of organizations with a high percentage of sales orders requiring no human intervention have a median perfect order performance rate of 96, compared to more manual sales order organizations that achieve a 90 perfect order index.
Reward accuracy. Automating sales order processing allows for measuring, tracking and rewarding first-time data accuracy for quotes and orders. Organizations that reward accuracy have a higher median perfect order performance rate of 98, compared to 86 for those that don’t.
Perfect May Be Possible
Achieving perfect order performance transcends silos inside the company, making everyone equally accountable for success in the eyes of the customer. But eliminating order errors doesn’t just make customers happy, it saves businesses money. Streamlined, automated warehousing processes result in more accurate orders and fewer returns, reducing the cost of processing returns or re-shipping what you didn’t get right the first time.
Order perfection is no longer a pie-in-the-sky ideal. For organizations that embrace the potential of technology to incrementally improve fulfillment processes, near-perfection, at least, is attainable.
If you still think that 100% perfect order performance is impossible, that doesn’t excuse you from trying to achieve it. You can’t lose by focusing more on the journey than the destination. Absolute perfection may indeed remain elusive, but striving for perfection will always be what separates the winners from the losers.
Marisa Brown is senior principal research lead, supply chain management with APQC (American Productivity & Quality Center). She focuses on the in-depth needs of APQC’s members in supply chain planning and supplier relationship management as she develops and oversees APQC's supply chain management research agenda. She leads APQC’s supply chain team that conducts research to provide insights into benchmarks, best practices and process improvements in supply chain planning, procurement, logistics, manufacturing, product development and innovation.
